• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.59-69
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• 2006

Pearson's correlation coefficient and Vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Food Science and Preservation
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• v.18 no.4
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• pp.546-551
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• 2011

Response surface methodology (RSM) was used for the microwave-assisted extraction (MAE) of the effective components of grape seed, such as its antioxidative and nitrite-scavenging abilities. Microwave power (2,450 MHz, 0-160W), ethanol concentration (0-100%), and MAE time (1-5 min) were used as independent variables (Xi) for the central composite design to yield 16 different MAE conditions. The optimum MAE conditions were predicted for the dependent variables of the extracts, such as the total phenolic content ($Y_1$) antioxidative ability ($Y_2$), and nitrite-scavenging ability ($Y_3$), depending on different microwave powers, ethanol concentrations, and MAE times. The determination coefficients ($R^2$) of the regression equations for the dependent variables ranged from 0.8024 to 0.9498. The maximal values of each dependent variable were predicted at different MAE conditions, as follows: 3.19% total phenolic content at 142.32W, 44.30% ethanol, and 4.36 min, and 1.22 antioxidative ability at 84.44W, 56.60% ethanol, and 3.28 min. More than 99.5% nitrite-scavenging ability was predicted at pH 1.2-3.0, 30.80-106.58W, 49.32-55.18% ethanol, and 3.72-4.58min, respectively. The results indicated that the total phenolic content and anti oxidative ability showed a higher correlation with each other in that they were more influenced by microwave power than by the other variables, while the nitrite-scavenging ability was largely influenced by the ethanol concentration.

• Journal of IKEEE
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• v.27 no.4
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• pp.486-493
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• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

• Journal of Haehwa Medicine
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• v.10 no.1
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• pp.483-487
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• 2001

Morus alba extract(MAE) was tested for its ability to inhibit alloxan induced lipidperoxidation. Lipid peroxide contents in serum, liver, kidney and heart were measured by the TBA method. ICR mice receiving alloxan at a dose of 6mg/kg intraperitoneally after a 24hrs starvation showed significantly increased lipid peroxide contents as compared to untreated control. Lipid peroxide contents in serum, liver, kidney of alloxan-diabetic mice were decreased by the treatment of MAE at the dose of 50mg/kg, 100mg/kg for 7 days.

• The Korean Nurse
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• v.28 no.5
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• pp.28-32
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• 1989

• The Korean Nurse
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• v.31 no.2
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• pp.45-50
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• 1992

• 한국호스피스협회지
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• s.44
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• pp.2-3
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• 2001

• Korean Journal of Hospice Care
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• v.2 no.1
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• pp.177-181
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• 2002